Furthermore, we provide a a theoretical proof of global convergence to the optimal solution of the likelihood function of Gaussian mixtures for one of the algorithms, namely MRAS-CD. This offers support that the algorithm is not merely an ad-hoc heuristic, but is systematically designed to produce global solutions to Gaussian mixture models. Finally, we investigate the fitness landscape of Gaussian mixture models and give evidence for why this is a difficult global optimization problem. We discuss different metrics that can be used to evaluate the difficulty of global optimization problems, and then apply them to the context of Gaussian mixture models.... to overcome local solutions in the mixture likelihood (see also Jank, 2006b; Tu et al., 2006). Along the same lines, Ueda and Nakano (1998) propose a deterministic annealing EM (DAEM) designed to overcome the local maxima problem 3.
|Title||:||Global Optimization of Finite Mixture Models|
|Author||:||Jeffrey W. Health|
|Publisher||:||ProQuest - 2007|